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Likelihood-Based Inference of B Cell Clonal Families

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PLoS Computational Biology

Public Library of Science

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      Abstract

      The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called “rearrangement” forming progenitor B cells, then a Darwinian process of lineage diversification and selection called “affinity maturation.” The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem “clonal family inference.” In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM) framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets.

      Author Summary

      Antibodies must recognize a great diversity of antigens to protect us from infectious disease. The binding properties of antibodies are determined by the DNA sequences of their corresponding B cell receptors (BCRs). These BCR sequences are created in naive form by VDJ recombination, which randomly selects and trims the ends of V, D, and J genes, then joins the resulting segments together with additional random nucleotides. If they pass initial screening and bind an antigen, these sequences then undergo an evolutionary process of reproduction, mutation, and selection, revising the BCR to improve binding to its cognate antigen. It has recently become possible to determine the BCR sequences resulting from this process in high throughput. Although these sequences implicitly contain a wealth of information about both antigen exposure and the process by which we learn to resist pathogens, this information can only be extracted using computer algorithms. In this paper we describe a likelihood-based statistical method to determine, given a collection of BCR sequences, which of them are derived from the same recombination events. It is based on a hidden Markov model (HMM) of VDJ rearrangement which is able to calculate likelihoods for many sequences at once.

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      FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

      Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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        Co-evolution of a broadly neutralizing HIV-1 antibody and founder virus

        Current HIV-1 vaccines elicit strain-specific neutralizing antibodies. However, cross-reactive neutralizing antibodies arise in ~20% of HIV-1-infected individuals, and details of their generation could provide a roadmap for effective vaccination. Here we report the isolation, evolution and structure of a broadly neutralizing antibody from an African donor followed from time of infection. The mature antibody, CH103, neutralized ~55% of HIV-1 isolates, and its co-crystal structure with gp120 revealed a novel loop-based mechanism of CD4-binding site recognition. Virus and antibody gene sequencing revealed concomitant virus evolution and antibody maturation. Notably, the CH103-lineage unmutated common ancestor avidly bound the transmitted/founder HIV-1 envelope glycoprotein, and evolution of antibody neutralization breadth was preceded by extensive viral diversification in and near the CH103 epitope. These data elucidate the viral and antibody evolution leading to induction of a lineage of HIV-1 broadly neutralizing antibodies and provide insights into strategies to elicit similar antibodies via vaccination.
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          Developmental pathway for potent V1V2-directed HIV-neutralizing antibodies.

          Antibodies capable of neutralizing HIV-1 often target variable regions 1 and 2 (V1V2) of the HIV-1 envelope, but the mechanism of their elicitation has been unclear. Here we define the developmental pathway by which such antibodies are generated and acquire the requisite molecular characteristics for neutralization. Twelve somatically related neutralizing antibodies (CAP256-VRC26.01-12) were isolated from donor CAP256 (from the Centre for the AIDS Programme of Research in South Africa (CAPRISA)); each antibody contained the protruding tyrosine-sulphated, anionic antigen-binding loop (complementarity-determining region (CDR) H3) characteristic of this category of antibodies. Their unmutated ancestor emerged between weeks 30-38 post-infection with a 35-residue CDR H3, and neutralized the virus that superinfected this individual 15 weeks after initial infection. Improved neutralization breadth and potency occurred by week 59 with modest affinity maturation, and was preceded by extensive diversification of the virus population. HIV-1 V1V2-directed neutralizing antibodies can thus develop relatively rapidly through initial selection of B cells with a long CDR H3, and limited subsequent somatic hypermutation. These data provide important insights relevant to HIV-1 vaccine development.
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            Author and article information

            Affiliations
            Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
            La Jolla Institute for Allergy and Immunology, UNITED STATES
            Author notes

            The authors have declared that no competing interests exist.

            • Conceived and designed the experiments: DKR FAM.

            • Performed the experiments: DKR.

            • Analyzed the data: DKR FAM.

            • Contributed reagents/materials/analysis tools: DKR.

            • Wrote the paper: DKR FAM.

            Contributors
            Role: Editor
            Journal
            PLoS Comput Biol
            PLoS Comput. Biol
            plos
            ploscomp
            PLoS Computational Biology
            Public Library of Science (San Francisco, CA USA )
            1553-734X
            1553-7358
            October 2016
            17 October 2016
            : 12
            : 10
            27749910
            5066976
            PCOMPBIOL-D-15-01705
            10.1371/journal.pcbi.1005086
            (Editor)
            © 2016 Ralph, Matsen

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Counts
            Figures: 13, Tables: 1, Pages: 28
            Product
            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
            Award ID: GM113246
            Award Recipient : ORCID: http://orcid.org/0000-0003-0607-6025
            Both authors were supported by National Institutes of Health R01 GM113246 (PI Matsen), R01 AI103981, and U19 AI117891. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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            Custom metadata
            The "Adaptive" data set is available at http://adaptivebiotech.com/link/mat2015. The "Vollmers" data set is available at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000656.v1.p1.

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